Overview

Dataset statistics

Number of variables23
Number of observations16376
Missing cells0
Missing cells (%)0.0%
Total size in memory2.9 MiB
Average record size in memory184.0 B

Variable types

Numeric21
Categorical2

Alerts

sum_services is highly correlated with services_frequency and 1 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 1 other fieldsHigh correlation
inter_avulsa is highly correlated with services_frequencyHigh correlation
prezao_semanal is highly correlated with sum_servicesHigh correlation
sum_services is highly correlated with services_frequency and 1 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 1 other fieldsHigh correlation
inter_avulsa is highly correlated with services_frequencyHigh correlation
prezao_semanal is highly correlated with sum_servicesHigh correlation
sum_services is highly correlated with services_frequencyHigh correlation
services_frequency is highly correlated with sum_servicesHigh correlation
sum_services is highly correlated with services_frequency and 2 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 3 other fieldsHigh correlation
inter_avulsa is highly correlated with sum_services and 1 other fieldsHigh correlation
clube is highly correlated with pct_internet_mensalHigh correlation
entretenimento is highly correlated with prezao_mensal and 2 other fieldsHigh correlation
pct_internet_mensal is highly correlated with clubeHigh correlation
prezao_diario is highly correlated with services_frequencyHigh correlation
prezao_mensal is highly correlated with entretenimentoHigh correlation
prezao_semanal is highly correlated with sum_servicesHigh correlation
servicos_operadora is highly correlated with services_frequencyHigh correlation
sms_internacional is highly correlated with entretenimento and 1 other fieldsHigh correlation
transf_entre_regionais is highly correlated with entretenimento and 1 other fieldsHigh correlation
antivirus is highly skewed (γ1 = 34.85832138) Skewed
app_emprego is highly skewed (γ1 = 29.21980657) Skewed
app_saude is highly skewed (γ1 = 32.83317862) Skewed
clube is highly skewed (γ1 = 50.13195303) Skewed
prezao_quinzenal is highly skewed (γ1 = 20.05624131) Skewed
recarga_sos is highly skewed (γ1 = 24.06023604) Skewed
sms_cobrar is highly skewed (γ1 = 52.74284097) Skewed
sms_internacional is highly skewed (γ1 = 22.32782473) Skewed
inter_avulsa has 7474 (45.6%) zeros Zeros
antivirus has 16290 (99.5%) zeros Zeros
app_educacao has 16256 (99.3%) zeros Zeros
app_emprego has 16303 (99.6%) zeros Zeros
app_saude has 16314 (99.6%) zeros Zeros
clube has 13180 (80.5%) zeros Zeros
pre_mix_giga has 16240 (99.2%) zeros Zeros
entretenimento has 14570 (89.0%) zeros Zeros
games has 16137 (98.5%) zeros Zeros
pct_internet_mensal has 15513 (94.7%) zeros Zeros
prezao_diario has 8736 (53.3%) zeros Zeros
prezao_mensal has 14370 (87.8%) zeros Zeros
prezao_quinzenal has 16295 (99.5%) zeros Zeros
prezao_semanal has 7924 (48.4%) zeros Zeros
recarga_sos has 16294 (99.5%) zeros Zeros
servicos_operadora has 12747 (77.8%) zeros Zeros
sms_cobrar has 16185 (98.8%) zeros Zeros
sms_internacional has 16109 (98.4%) zeros Zeros
truecaller has 16174 (98.8%) zeros Zeros

Reproduction

Analysis started2022-03-22 22:37:13.408419
Analysis finished2022-03-22 22:37:52.025808
Duration38.62 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

sum_services
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8083
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.54977101
Minimum0.01
Maximum1202.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:52.078724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.47
Q119.98
median46.18
Q3100.1125
95-th percentile194.87
Maximum1202.26
Range1202.25
Interquartile range (IQR)80.1325

Descriptive statistics

Standard deviation68.74195725
Coefficient of variation (CV)1.002803602
Kurtosis13.44805669
Mean68.54977101
Median Absolute Deviation (MAD)32.485
Skewness2.465129991
Sum1122571.05
Variance4725.456687
MonotonicityNot monotonic
2022-03-22T19:37:52.166338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.99422
 
2.6%
19.98221
 
1.3%
19.99164
 
1.0%
29.97119
 
0.7%
39.9673
 
0.4%
1.9972
 
0.4%
1.4972
 
0.4%
129.8770
 
0.4%
44.9764
 
0.4%
2.9862
 
0.4%
Other values (8073)15037
91.8%
ValueCountFrequency (%)
0.012
 
< 0.1%
0.024
< 0.1%
0.031
 
< 0.1%
0.043
< 0.1%
0.055
< 0.1%
0.062
 
< 0.1%
0.081
 
< 0.1%
0.093
< 0.1%
0.112
 
< 0.1%
0.123
< 0.1%
ValueCountFrequency (%)
1202.261
< 0.1%
885.591
< 0.1%
772.471
< 0.1%
691.631
< 0.1%
671.961
< 0.1%
669.541
< 0.1%
647.071
< 0.1%
632.841
< 0.1%
629.581
< 0.1%
6111
< 0.1%

services_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct173
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.65138007
Minimum1
Maximum448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:52.251060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median12
Q324
95-th percentile59
Maximum448
Range447
Interquartile range (IQR)19

Descriptive statistics

Standard deviation21.59358786
Coefficient of variation (CV)1.157747458
Kurtosis25.21450875
Mean18.65138007
Median Absolute Deviation (MAD)8
Skewness3.462621231
Sum305435
Variance466.2830369
MonotonicityNot monotonic
2022-03-22T19:37:52.330789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11128
 
6.9%
3934
 
5.7%
2919
 
5.6%
4794
 
4.8%
5690
 
4.2%
6668
 
4.1%
7636
 
3.9%
8592
 
3.6%
13538
 
3.3%
9516
 
3.2%
Other values (163)8961
54.7%
ValueCountFrequency (%)
11128
6.9%
2919
5.6%
3934
5.7%
4794
4.8%
5690
4.2%
6668
4.1%
7636
3.9%
8592
3.6%
9516
3.2%
10453
2.8%
ValueCountFrequency (%)
4481
< 0.1%
3281
< 0.1%
2581
< 0.1%
2431
< 0.1%
2331
< 0.1%
2321
< 0.1%
2311
< 0.1%
2241
< 0.1%
2191
< 0.1%
2161
< 0.1%

inter_avulsa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct157
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.404433317
Minimum0
Maximum447
Zeros7474
Zeros (%)45.6%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:52.414508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile43
Maximum447
Range447
Interquartile range (IQR)8

Descriptive statistics

Standard deviation18.05257198
Coefficient of variation (CV)2.147982059
Kurtosis42.0884869
Mean8.404433317
Median Absolute Deviation (MAD)1
Skewness4.669133181
Sum137631
Variance325.895355
MonotonicityNot monotonic
2022-03-22T19:37:52.494242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07474
45.6%
11371
 
8.4%
2821
 
5.0%
3624
 
3.8%
4531
 
3.2%
5426
 
2.6%
6410
 
2.5%
7353
 
2.2%
8305
 
1.9%
9294
 
1.8%
Other values (147)3767
23.0%
ValueCountFrequency (%)
07474
45.6%
11371
 
8.4%
2821
 
5.0%
3624
 
3.8%
4531
 
3.2%
5426
 
2.6%
6410
 
2.5%
7353
 
2.2%
8305
 
1.9%
9294
 
1.8%
ValueCountFrequency (%)
4471
< 0.1%
2191
< 0.1%
2151
< 0.1%
2111
< 0.1%
2051
< 0.1%
2001
< 0.1%
1971
< 0.1%
1841
< 0.1%
1811
< 0.1%
1761
< 0.1%

antivirus
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01618221788
Minimum0
Maximum21
Zeros16290
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:52.568041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32359731
Coefficient of variation (CV)19.99709264
Kurtosis1637.488782
Mean0.01618221788
Median Absolute Deviation (MAD)0
Skewness34.85832138
Sum265
Variance0.1047152191
MonotonicityNot monotonic
2022-03-22T19:37:52.625802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
016290
99.5%
136
 
0.2%
214
 
0.1%
311
 
0.1%
59
 
0.1%
47
 
< 0.1%
63
 
< 0.1%
122
 
< 0.1%
211
 
< 0.1%
81
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
016290
99.5%
136
 
0.2%
214
 
0.1%
311
 
0.1%
47
 
< 0.1%
59
 
0.1%
63
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
63
 
< 0.1%
59
0.1%
47
< 0.1%
311
0.1%
214
0.1%

app_educacao
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01917440156
Minimum0
Maximum10
Zeros16256
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.023473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2765081325
Coefficient of variation (CV)14.42069165
Kurtosis422.3448772
Mean0.01917440156
Median Absolute Deviation (MAD)0
Skewness18.95147522
Sum314
Variance0.07645674735
MonotonicityNot monotonic
2022-03-22T19:37:53.083273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016256
99.3%
147
 
0.3%
228
 
0.2%
312
 
0.1%
412
 
0.1%
68
 
< 0.1%
58
 
< 0.1%
73
 
< 0.1%
101
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
016256
99.3%
147
 
0.3%
228
 
0.2%
312
 
0.1%
412
 
0.1%
58
 
< 0.1%
68
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
68
 
< 0.1%
58
 
< 0.1%
412
 
0.1%
312
 
0.1%
228
 
0.2%
147
 
0.3%
016256
99.3%

app_emprego
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01581582804
Minimum0
Maximum16
Zeros16303
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.144071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.324181054
Coefficient of variation (CV)20.49725459
Kurtosis1023.737651
Mean0.01581582804
Median Absolute Deviation (MAD)0
Skewness29.21980657
Sum259
Variance0.1050933558
MonotonicityNot monotonic
2022-03-22T19:37:53.204868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
016303
99.6%
128
 
0.2%
210
 
0.1%
39
 
0.1%
47
 
< 0.1%
74
 
< 0.1%
54
 
< 0.1%
63
 
< 0.1%
132
 
< 0.1%
102
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
016303
99.6%
128
 
0.2%
210
 
0.1%
39
 
0.1%
47
 
< 0.1%
54
 
< 0.1%
63
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
132
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%
74
< 0.1%
63
< 0.1%
54
< 0.1%
47
< 0.1%

app_saude
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01141914998
Minimum0
Maximum13
Zeros16314
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.266659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2602247553
Coefficient of variation (CV)22.78845237
Kurtosis1258.119659
Mean0.01141914998
Median Absolute Deviation (MAD)0
Skewness32.83317862
Sum187
Variance0.06771692329
MonotonicityNot monotonic
2022-03-22T19:37:53.330451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
016314
99.6%
128
 
0.2%
212
 
0.1%
35
 
< 0.1%
45
 
< 0.1%
83
 
< 0.1%
62
 
< 0.1%
52
 
< 0.1%
92
 
< 0.1%
131
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
016314
99.6%
128
 
0.2%
212
 
0.1%
35
 
< 0.1%
45
 
< 0.1%
52
 
< 0.1%
62
 
< 0.1%
83
 
< 0.1%
92
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
92
 
< 0.1%
83
 
< 0.1%
62
 
< 0.1%
52
 
< 0.1%
45
< 0.1%
35
< 0.1%
212
0.1%

clube
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3760991695
Minimum0
Maximum155
Zeros13180
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.393236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum155
Range155
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.715855209
Coefficient of variation (CV)4.562241419
Kurtosis4180.218843
Mean0.3760991695
Median Absolute Deviation (MAD)0
Skewness50.13195303
Sum6159
Variance2.944159097
MonotonicityNot monotonic
2022-03-22T19:37:53.458020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
013180
80.5%
12023
 
12.4%
2621
 
3.8%
3242
 
1.5%
495
 
0.6%
566
 
0.4%
655
 
0.3%
731
 
0.2%
916
 
0.1%
129
 
0.1%
Other values (12)38
 
0.2%
ValueCountFrequency (%)
013180
80.5%
12023
 
12.4%
2621
 
3.8%
3242
 
1.5%
495
 
0.6%
566
 
0.4%
655
 
0.3%
731
 
0.2%
88
 
< 0.1%
916
 
0.1%
ValueCountFrequency (%)
1551
 
< 0.1%
671
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
201
 
< 0.1%
161
 
< 0.1%
152
 
< 0.1%
143
 
< 0.1%
134
< 0.1%
129
0.1%

pre_mix_giga
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07010258915
Minimum0
Maximum43
Zeros16240
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.541740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9089721476
Coefficient of variation (CV)12.96631349
Kurtosis482.1004591
Mean0.07010258915
Median Absolute Deviation (MAD)0
Skewness18.09212166
Sum1148
Variance0.826230365
MonotonicityNot monotonic
2022-03-22T19:37:53.603533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
016240
99.2%
1319
 
0.1%
1212
 
0.1%
1011
 
0.1%
611
 
0.1%
511
 
0.1%
1111
 
0.1%
410
 
0.1%
210
 
0.1%
78
 
< 0.1%
Other values (9)33
 
0.2%
ValueCountFrequency (%)
016240
99.2%
17
 
< 0.1%
210
 
0.1%
37
 
< 0.1%
410
 
0.1%
511
 
0.1%
611
 
0.1%
78
 
< 0.1%
84
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
431
 
< 0.1%
271
 
< 0.1%
172
 
< 0.1%
151
 
< 0.1%
144
 
< 0.1%
1319
0.1%
1212
0.1%
1111
0.1%
1011
0.1%
96
 
< 0.1%

entretenimento
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5950781632
Minimum0
Maximum40
Zeros14570
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.678283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.344888393
Coefficient of variation (CV)3.940471249
Kurtosis41.87825215
Mean0.5950781632
Median Absolute Deviation (MAD)0
Skewness5.662629935
Sum9745
Variance5.498501576
MonotonicityNot monotonic
2022-03-22T19:37:53.755027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
014570
89.0%
1395
 
2.4%
2265
 
1.6%
3200
 
1.2%
4170
 
1.0%
5124
 
0.8%
699
 
0.6%
782
 
0.5%
1376
 
0.5%
871
 
0.4%
Other values (22)324
 
2.0%
ValueCountFrequency (%)
014570
89.0%
1395
 
2.4%
2265
 
1.6%
3200
 
1.2%
4170
 
1.0%
5124
 
0.8%
699
 
0.6%
782
 
0.5%
871
 
0.4%
960
 
0.4%
ValueCountFrequency (%)
401
 
< 0.1%
381
 
< 0.1%
341
 
< 0.1%
291
 
< 0.1%
272
 
< 0.1%
263
< 0.1%
253
< 0.1%
246
< 0.1%
232
 
< 0.1%
222
 
< 0.1%

games
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03529555447
Minimum0
Maximum15
Zeros16137
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.822801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4004271894
Coefficient of variation (CV)11.34497518
Kurtosis421.3665179
Mean0.03529555447
Median Absolute Deviation (MAD)0
Skewness18.1380017
Sum578
Variance0.160341934
MonotonicityNot monotonic
2022-03-22T19:37:53.883597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
016137
98.5%
1120
 
0.7%
246
 
0.3%
330
 
0.2%
414
 
0.1%
58
 
< 0.1%
65
 
< 0.1%
85
 
< 0.1%
103
 
< 0.1%
113
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
016137
98.5%
1120
 
0.7%
246
 
0.3%
330
 
0.2%
414
 
0.1%
58
 
< 0.1%
65
 
< 0.1%
72
 
< 0.1%
85
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
113
 
< 0.1%
103
 
< 0.1%
92
 
< 0.1%
85
 
< 0.1%
72
 
< 0.1%
65
 
< 0.1%
58
 
< 0.1%
414
0.1%
330
0.2%

pct_internet_mensal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09452857841
Minimum0
Maximum15
Zeros15513
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:53.956355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5412831665
Coefficient of variation (CV)5.726132516
Kurtosis164.1668929
Mean0.09452857841
Median Absolute Deviation (MAD)0
Skewness10.5229222
Sum1548
Variance0.2929874663
MonotonicityNot monotonic
2022-03-22T19:37:54.016154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
015513
94.7%
1556
 
3.4%
2145
 
0.9%
387
 
0.5%
429
 
0.2%
516
 
0.1%
69
 
0.1%
88
 
< 0.1%
74
 
< 0.1%
93
 
< 0.1%
Other values (5)6
 
< 0.1%
ValueCountFrequency (%)
015513
94.7%
1556
 
3.4%
2145
 
0.9%
387
 
0.5%
429
 
0.2%
516
 
0.1%
69
 
0.1%
74
 
< 0.1%
88
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
141
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
88
< 0.1%
74
 
< 0.1%
69
0.1%
516
0.1%

prezao_diario
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct98
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.655532487
Minimum0
Maximum134
Zeros8736
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.089909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile22
Maximum134
Range134
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.634188038
Coefficient of variation (CV)2.069406253
Kurtosis24.32789976
Mean4.655532487
Median Absolute Deviation (MAD)0
Skewness4.084420719
Sum76239
Variance92.81757916
MonotonicityNot monotonic
2022-03-22T19:37:54.176617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08736
53.3%
11047
 
6.4%
2816
 
5.0%
3681
 
4.2%
4545
 
3.3%
5538
 
3.3%
6429
 
2.6%
7387
 
2.4%
8319
 
1.9%
9276
 
1.7%
Other values (88)2602
 
15.9%
ValueCountFrequency (%)
08736
53.3%
11047
 
6.4%
2816
 
5.0%
3681
 
4.2%
4545
 
3.3%
5538
 
3.3%
6429
 
2.6%
7387
 
2.4%
8319
 
1.9%
9276
 
1.7%
ValueCountFrequency (%)
1341
< 0.1%
1301
< 0.1%
1091
< 0.1%
1051
< 0.1%
1031
< 0.1%
1021
< 0.1%
971
< 0.1%
962
< 0.1%
921
< 0.1%
912
< 0.1%

prezao_mensal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2537860283
Minimum0
Maximum18
Zeros14370
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.245388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8064346286
Coefficient of variation (CV)3.177616332
Kurtosis32.25618017
Mean0.2537860283
Median Absolute Deviation (MAD)0
Skewness4.424935011
Sum4156
Variance0.6503368102
MonotonicityNot monotonic
2022-03-22T19:37:54.305190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
014370
87.8%
1813
 
5.0%
2555
 
3.4%
3471
 
2.9%
490
 
0.5%
541
 
0.3%
620
 
0.1%
77
 
< 0.1%
84
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
014370
87.8%
1813
 
5.0%
2555
 
3.4%
3471
 
2.9%
490
 
0.5%
541
 
0.3%
620
 
0.1%
77
 
< 0.1%
84
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
94
 
< 0.1%
84
 
< 0.1%
77
 
< 0.1%
620
 
0.1%
541
 
0.3%
490
 
0.5%
3471
2.9%
2555
3.4%
1813
5.0%

prezao_quinzenal
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02167806546
Minimum0
Maximum14
Zeros16295
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.363995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3534177112
Coefficient of variation (CV)16.30300969
Kurtosis486.393935
Mean0.02167806546
Median Absolute Deviation (MAD)0
Skewness20.05624131
Sum355
Variance0.1249040786
MonotonicityNot monotonic
2022-03-22T19:37:54.419806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
016295
99.5%
317
 
0.1%
615
 
0.1%
112
 
0.1%
510
 
0.1%
79
 
0.1%
49
 
0.1%
25
 
< 0.1%
82
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
016295
99.5%
112
 
0.1%
25
 
< 0.1%
317
 
0.1%
49
 
0.1%
510
 
0.1%
615
 
0.1%
79
 
0.1%
82
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
131
 
< 0.1%
82
 
< 0.1%
79
0.1%
615
0.1%
510
0.1%
49
0.1%
317
0.1%
25
 
< 0.1%
112
0.1%

prezao_semanal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.09062042
Minimum0
Maximum39
Zeros7924
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.495553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile13
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.614504311
Coefficient of variation (CV)1.493067308
Kurtosis4.303137982
Mean3.09062042
Median Absolute Deviation (MAD)1
Skewness1.887970321
Sum50612
Variance21.29365003
MonotonicityNot monotonic
2022-03-22T19:37:54.569304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
07924
48.4%
11545
 
9.4%
21121
 
6.8%
3854
 
5.2%
4684
 
4.2%
5589
 
3.6%
13503
 
3.1%
6483
 
2.9%
7415
 
2.5%
12402
 
2.5%
Other values (27)1856
 
11.3%
ValueCountFrequency (%)
07924
48.4%
11545
 
9.4%
21121
 
6.8%
3854
 
5.2%
4684
 
4.2%
5589
 
3.6%
6483
 
2.9%
7415
 
2.5%
8384
 
2.3%
9338
 
2.1%
ValueCountFrequency (%)
391
 
< 0.1%
381
 
< 0.1%
372
 
< 0.1%
362
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
307
< 0.1%
298
< 0.1%
286
< 0.1%
276
< 0.1%

recarga_sos
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011358085
Minimum0
Maximum8
Zeros16294
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.638077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19860389
Coefficient of variation (CV)17.48568442
Kurtosis691.2109646
Mean0.011358085
Median Absolute Deviation (MAD)0
Skewness24.06023604
Sum186
Variance0.03944350511
MonotonicityNot monotonic
2022-03-22T19:37:54.698871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
016294
99.5%
134
 
0.2%
227
 
0.2%
46
 
< 0.1%
36
 
< 0.1%
64
 
< 0.1%
52
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
016294
99.5%
134
 
0.2%
227
 
0.2%
36
 
< 0.1%
46
 
< 0.1%
52
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
72
 
< 0.1%
64
 
< 0.1%
52
 
< 0.1%
46
 
< 0.1%
36
 
< 0.1%
227
 
0.2%
134
 
0.2%
016294
99.5%

servicos_operadora
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6511358085
Minimum0
Maximum36
Zeros12747
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.766646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.245891093
Coefficient of variation (CV)3.44918996
Kurtosis44.37784879
Mean0.6511358085
Median Absolute Deviation (MAD)0
Skewness5.90642401
Sum10663
Variance5.044026801
MonotonicityNot monotonic
2022-03-22T19:37:54.835415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
012747
77.8%
12298
 
14.0%
2419
 
2.6%
3174
 
1.1%
4111
 
0.7%
572
 
0.4%
866
 
0.4%
1263
 
0.4%
1361
 
0.4%
761
 
0.4%
Other values (22)304
 
1.9%
ValueCountFrequency (%)
012747
77.8%
12298
 
14.0%
2419
 
2.6%
3174
 
1.1%
4111
 
0.7%
572
 
0.4%
656
 
0.3%
761
 
0.4%
866
 
0.4%
947
 
0.3%
ValueCountFrequency (%)
361
 
< 0.1%
351
 
< 0.1%
312
 
< 0.1%
301
 
< 0.1%
282
 
< 0.1%
271
 
< 0.1%
252
 
< 0.1%
242
 
< 0.1%
231
 
< 0.1%
226
< 0.1%

sms_cobrar
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04683683439
Minimum0
Maximum79
Zeros16185
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:54.906178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum79
Range79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.122512888
Coefficient of variation (CV)23.9664551
Kurtosis3329.04267
Mean0.04683683439
Median Absolute Deviation (MAD)0
Skewness52.74284097
Sum767
Variance1.260035185
MonotonicityNot monotonic
2022-03-22T19:37:54.970962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
016185
98.8%
1112
 
0.7%
225
 
0.2%
311
 
0.1%
49
 
0.1%
69
 
0.1%
55
 
< 0.1%
84
 
< 0.1%
73
 
< 0.1%
792
 
< 0.1%
Other values (9)11
 
0.1%
ValueCountFrequency (%)
016185
98.8%
1112
 
0.7%
225
 
0.2%
311
 
0.1%
49
 
0.1%
55
 
< 0.1%
69
 
0.1%
73
 
< 0.1%
84
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
792
< 0.1%
491
< 0.1%
331
< 0.1%
292
< 0.1%
221
< 0.1%
172
< 0.1%
151
< 0.1%
141
< 0.1%
121
< 0.1%
91
< 0.1%

sms_internacional
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0268075232
Minimum0
Maximum14
Zeros16109
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:55.034750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2816777973
Coefficient of variation (CV)10.50741597
Kurtosis793.7408493
Mean0.0268075232
Median Absolute Deviation (MAD)0
Skewness22.32782473
Sum439
Variance0.07934238152
MonotonicityNot monotonic
2022-03-22T19:37:55.092607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
016109
98.4%
1180
 
1.1%
255
 
0.3%
313
 
0.1%
49
 
0.1%
54
 
< 0.1%
62
 
< 0.1%
131
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
016109
98.4%
1180
 
1.1%
255
 
0.3%
313
 
0.1%
49
 
0.1%
54
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
131
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
62
 
< 0.1%
54
 
< 0.1%
49
 
0.1%
313
 
0.1%
255
 
0.3%
1180
1.1%

transf_entre_regionais
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
16278 
 
97
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Common Values

ValueCountFrequency (%)
16278
99.4%
97
 
0.6%
1
 
< 0.1%

Length

2022-03-22T19:37:55.171298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:55.214201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16278
99.4%
97
 
0.6%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

truecaller
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02699071812
Minimum0
Maximum9
Zeros16174
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size128.1 KiB
2022-03-22T19:37:55.255012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3064766634
Coefficient of variation (CV)11.35489104
Kurtosis308.1931786
Mean0.02699071812
Median Absolute Deviation (MAD)0
Skewness15.92348546
Sum442
Variance0.0939279452
MonotonicityNot monotonic
2022-03-22T19:37:55.310872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016174
98.8%
1105
 
0.6%
237
 
0.2%
322
 
0.1%
417
 
0.1%
59
 
0.1%
65
 
< 0.1%
74
 
< 0.1%
92
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
016174
98.8%
1105
 
0.6%
237
 
0.2%
322
 
0.1%
417
 
0.1%
59
 
0.1%
65
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
74
 
< 0.1%
65
 
< 0.1%
59
 
0.1%
417
 
0.1%
322
 
0.1%
237
 
0.2%
1105
 
0.6%
016174
98.8%

venda
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
11542 
4834 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
11542
70.5%
4834
29.5%

Length

2022-03-22T19:37:55.379600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T19:37:55.420464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
11542
70.5%
4834
29.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-22T19:37:50.007556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:14.383656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.362042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:17.913853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.473640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.398263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.069620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:24.846675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:26.802142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.495477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.119048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.165207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:33.843599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.541918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:37.541284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.205670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:40.879076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:42.966097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:44.636516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.322875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:48.356078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:50.088286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:14.466379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.436792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:17.984620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.548401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.469964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.151345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:24.918438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:26.881871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.572220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.197785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.246934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:33.919345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.619658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:37.622961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.280422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:40.967778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:43.041844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:44.718239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.398622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:48.434872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:50.158052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:14.538140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.507555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.052391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.617159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.539731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.229083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:24.997172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:26.963652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.648968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.272535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.320688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:33.992099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.691418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:37.699704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.355169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:41.046516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:43.114601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:44.795979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.471378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:48.520533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:50.225826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:14.607906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.577322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.123154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.684932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.611492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.304830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:25.066939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:27.041338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.719727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.348282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.396434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:34.063862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.764175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:37.783424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.429924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:41.129243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:43.186361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:44.872725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.549118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:48.602254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:50.290609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:14.682656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.646092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.191979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.750713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.681260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.383567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:25.136706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:27.114094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.790490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.419045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.481152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:34.135620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.835935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:37.856181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.499686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:41.206978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:43.258121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:44.952458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.623869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:48.672022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:50.360378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:15.097270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.718850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.259697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.821476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.761993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.465298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:25.220426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:27.191835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.862250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.492799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.557894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:34.211367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.908692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:37.929934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.572443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:41.286760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:43.331878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:45.030200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.695628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:48.745775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:50.435127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:15.179994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.796589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.337437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.901209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.856672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.551007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:25.300163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:27.276554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:28.949957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.573529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.639622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:34.298078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:35.994410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:38.011661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.651180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:41.377409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:43.412605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:45.116963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:46.783390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:50.510873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:15.256737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:16.868349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.409198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:19.976957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:21.939396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:23.631739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:25.378897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:27.363262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:29.027697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:30.649276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:32.717361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:34.381799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:36.067162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:16.945092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:18.487936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:23.716455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:32.799089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:34.466572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:36.145899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:38.171129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:39.802674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:29.901777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:38.966469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:46.093642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:49.762375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:22.919167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:24.681228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-22T19:37:26.656631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:40.799341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-22T19:37:49.937789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-22T19:37:55.652687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-22T19:37:55.827099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-22T19:37:55.981585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-22T19:37:56.069292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.